The identification and characterization of binding sites is a critical component of structure-based drug design (SBDD). Probe-based/cosolvent molecular dynamics (MD) methods that allow for protein flexibility have been developed to predict ligand binding sites. However, cryptic pockets that appear only upon ligand binding and occluded binding sites with no access to the solvent pose significant challenges to these methods. Here, we report the development of accelerated ligand-mapping MD (aLMMD), which combines accelerated MD with LMMD, for the detection of these challenging binding sites. The method was validated on five proteins with what we term "recalcitrant" cryptic pockets, which are deeply buried pockets that require extensive movement of the protein backbone to expose, and three proteins with occluded binding sites. In all the cases, aLMMD was able to detect and sample the binding sites. Our results suggest that aLMMD could be used as a general approach for the detection of such elusive binding sites in protein targets, thus providing valuable information for SBDD.

The extracellular transporter, lipocalin-type prostaglandin D synthase (L-PGDS) binds to heme and heme metabolites with high affinity. It has been reported that L-PGDS protects neuronal cells against apoptosis induced by exposure to hydrogen peroxide. Our study demonstrates that when human WT L-PGDS is in complex with heme, it exhibits a strong peroxidase activity thus behaving as a pseudo-peroxidase. Electron paramagnetic resonance studies confirm that heme in the L-PGDS-heme complex is hexacoordinated with high-spin Fe(III). NMR titration of heme in L-PGDS points to hydrophobic interaction between heme and several residues within the β-barrel cavity of L-PGDS. In addition to the transporter function, L-PGDS is a key amyloid β chaperone in human cerebrospinal fluid. The presence of high levels of bilirubin and its derivatives, implicated in Alzheimer's disease, by binding to L-PGDS may reduce its chaperone activity. Nevertheless, our ThT binding assay establishes that heme and heme metabolites do not significantly alter the neuroprotective chaperone function of L-PGDS. Guided by NMR data we reconstructed the heme L-PGDS complex using extensive molecular dynamics simulations providing a platform for mechanistic interpretation of the catalytic and transporting functions and their modulation by secondary ligands like Aβ peptides and heme metabolites.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic poses serious threats to the global public health and leads to an unprecedented worldwide crisis. Unfortunately, no effective drugs or vaccines are available till now. Since the RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 is a promising therapeutic target, a deep learning and molecular simulation based hybrid drug screening procedure was proposed and applied to identify potential drug candidates targeting RdRp from 1906 approved drugs. Among the four selected FDA-approved drug candidates, Pralatrexate and Azithromycin were confirmed to effectively inhibit SARS-CoV-2 replication in vitro with EC 50 values of 0.008µM and 9.453 µM, respectively. For the first time, our study discovered that Pralatrexate is able to potently inhibit SARS-CoV-2 replication with a stronger inhibitory activity than Remdesivir within the same experimental conditions. The paper demonstrates the feasibility of accurate virtual drug screening for inhibitors of SARS-CoV-2 and provides potential therapeutic agents against COVID-19.

Accurate identification of ligand-binding pockets in a protein is important for structure-based drug design. In recent years, several deep learning models were developed to learn important physical–chemical and spatial information to predict ligand-binding pockets in a protein. However, ranking the native ligand binding pockets from a pool of predicted pockets is still a hard task for computational molecular biologists using a single web-based tool. Hence, we believe, by using closer to real application data set as training and by providing ligand information, an enhanced model to identify accurate pockets can be obtained. In this article, we propose a new deep learning method called DeepBindPoc for identifying and ranking ligand-binding pockets in proteins. The model is built by using information about the binding pocket and associated ligand. We take advantage of the mol2vec tool to represent both the given ligand and pocket as vectors to construct a densely fully connected layer model. During the training, important features for pocket-ligand binding are automatically extracted and high-level information is preserved appropriately. DeepBindPoc demonstrated a strong complementary advantage for the detection of native-like pockets when combined with traditional popular methods, such as fpocket and P2Rank. The proposed method is extensively tested and validated with standard procedures on multiple datasets, including a dataset with G-protein Coupled receptors. The systematic testing and validation of our method suggest that DeepBindPoc is a valuable tool to rank near-native pockets for theoretically modeled protein with unknown experimental active site but have known ligand. The DeepBindPoc model described in this article is available at GitHub ( https://github.com/haiping1010/DeepBindPoc ) and the webserver is available at ( http://cbblab.siat.ac.cn/DeepBindPoc/index.php ).

The recently discovered 340-cavity in influenza neuraminidase (NA) N6 and N7 subtypes has introduced new possibilities for rational structure-based drug design. However, the plasticity of the 340-loop (residues 342–347) and the role of the 340-loop in NA activity and substrate binding have not been deeply exploited. Here, we investigate the mechanism of 340-cavity formation and demonstrate for the first time that seven of nine NA subtypes are able to adopt an open 340-cavity over 1.8 μs total molecular dynamics simulation time. The finding that the 340-loop plays a role in the sialic acid binding pathway suggests that the 340-cavity can function as a druggable pocket. Comparing the open and closed conformations of the 340-loop, the side chain orientation of residue 344 was found to govern the formation of the 340-cavity. Additionally, the conserved calcium ion was found to substantially influence the stability of the 340-loop. Our study provides dynamical evidence supporting the 340-cavity as a druggable hotspot at the atomic level and offers new structural insight in designing antiviral drugs.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus poses serious threats to the global public health and leads to worldwide crisis. No effective drug or vaccine is readily available. The viral RNA-dependent RNA polymerase (RdRp) is a promising therapeutic target. A hybrid drug screening procedure was proposed and applied to identify potential drug candidates targeting RdRp from 1906 approved drugs. Among the four selected market available drug candidates, Pralatrexate and Azithromycin were confirmed to effectively inhibit SARS-CoV-2 replication in vitro with EC50 values of 0.008μM and 9.453 μM, respectively. For the first time, our study discovered that Pralatrexate is able to potently inhibit SARS-CoV-2 replication with a stronger inhibitory activity than Remdesivir within the same experimental conditions. The paper demonstrates the feasibility of fast and accurate anti-viral drug screening for inhibitors of SARS-CoV-2 and provides potential therapeutic agents against COVID-19.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic poses serious threats to the global public health and leads to an unprecedented worldwide crisis. Unfortunately, no effective drugs or vaccines are available till now. Since the RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 is a promising therapeutic target, a deep learning and molecular simulation based hybrid drug screening procedure was proposed and applied to identify potential drug candidates targeting RdRp from 1906 approved drugs. Among the four selected FDA-approved drug candidates, Pralatrexate and Azithromycin were confirmed to effectively inhibit SARS-CoV-2 replication in vitro with EC50 values of 0.008µM and 9.453 µM, respectively. For the first time, our study discovered that Pralatrexate is able to potently inhibit SARS-CoV-2 replication with a stronger inhibitory activity than Remdesivir within the same experimental conditions. The paper demonstrates the feasibility of accurate virtual drug screening for inhibitors of SARS-CoV-2 and provides potential therapeutic agents against COVID-19.

Profilins are abundant cytosolic proteins that are universally expressed in eukaryotes and that regulate actin filament elongation by binding to both monomeric actin (G-actin) and formin proteins. The atypical profilin Arabidopsis AtPRF3 has been reported to cooperate with canonical profilin isoforms in suppressing formin-mediated actin polymerization during plant innate immunity responses. AtPRF3 has a 37-amino acid-long N-terminal extension (NTE), and its suppressive effect on actin assembly is derived from enhanced interaction with the polyproline (Poly-P) of the formin AtFH1. However, the molecular mechanism remains unclear. Here, we solved the crystal structures of AtPRF3Δ22 and AtPRF3Δ37, as well as AtPRF2 apo form and in complex with AtFH1 Poly-P at 1.5-3.6 Å resolutions. By combining these structures with molecular modeling, we found that AtPRF3Δ22 NTE has high plasticity, with a primary "closed" conformation that can adopt an open conformation that enables Poly-P binding. Furthermore, using molecular dynamics simulation and free-energy calculations of protein-protein binding, along with experimental validation, we show that the AtPRF3Δ22 binds to Poly-P in an adaptive manner, thereby enabling different binding modes that maintain the interaction through disordered sequences. Together, our structural and simulation results suggest that the dynamic conformational changes of the AtPRF3 NTE upon Poly-P binding modulate their interactions to fine-tune formin-mediated actin assembly.

Characterization and studies of proteome are challenging because biological samples are complex, with a wide dynamic range of abundance. At present the proteins are identified by digestion into peptides, with subsequent identification of the peptides by mass spectrometry (MS). MS is a powerful technique for the purpose, but it cannot identify every peptide in such complex mixtures simultaneously. For accurate analysis and quantification it is important to separate the peptides first by chro- matography into fractions of a size that MS can handle. With these less complex fractions, the probability is increased of identi- fying peptides of low abundance that would otherwise experience ion suppression effects due to the presence of peptides of high abundance. Enrichment for peptides with certain post-translational modifications helps to increase their detection rates as well. Electrostatic repulsion-hydrophilic interaction chromatography (ERLIC) is a mixed-mode chromatographic technique which combines the use of electrostatic repulsion and hydrophilic interaction. This review provides an overview of ERLIC and its var- ious proteomics applications. ERLIC has been demonstrated to have good orthogonality to reverse phase liquid chromatography (RPLC), making it useful as a first dimension in multidimensional liquid chromatography (MDLC) and fractionation of digests in general. Peptides elute in order of their isoelectric points and polarity. ERLIC has also been successfully utilized for the enrichment for phosphopeptides and glycopeptides, facilitating their identification. In addition, it is promising for the study of peptide deamidation. ERLIC performs comparably well or better than established methods for these various applications, and serves as a viable and efficient workflow alternative.